[go: up one dir, main page]

Skip to main content

Advertisement

Log in

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Audy J. (2006) An appraisal of techniques and equipment for cutting force measurement. Journal of Zhejiang University Science A 7: 1781–1789

    Article  Google Scholar 

  • Ben-Haim, Y. (2001). Information-gap decision theory: Decisions under severe uncertainty. Academic Press.

  • Berger B. S., Minis I., Harley J., Rokin M., Papadopoulos M. (1998) Wavelet based cutting state identification. Journal of Sound and Vibration 213(5): 813–827

    Article  Google Scholar 

  • Byrne G., Dornfeld D., Inasaki I., Ketteler G., König W., Teti R. (1995) Tool condition monitoring (TCM)—The status of research and industrial application. Annals of the CIRP 44(2): 541–567

    Article  Google Scholar 

  • Chao P., Hwang Y. D. (1997) An improved neural network model for the prediction of cutting tool life. Journal of Intelligent Manufacturing 8: 107–115

    Article  Google Scholar 

  • Da Z. J., Sadler J. P., Jawahir I. S. (1997) Prediction of optimum cutting conditions for turning operations at varying tool-wear states. Transaction of NAMRI/SME XXV: 75–80

    Google Scholar 

  • Deb K. (1996) Optimization for engineering design. Prentice Hall of India (p) Ltd, New Delhi

    Google Scholar 

  • Dornfeld D. A. (1990) Neural network sensor fusion for tool condition monitoring. Annals of the CIRP 39(1): 101–105

    Article  Google Scholar 

  • Franco-Gasca L. A., Herrera-Ruiz G., Peniche-Vera R., Romero-Troncoso R. D. J., Leal-Tafolla W. (2006) Sensorless tool failure monitoring system for drilling machines. International Journal of Machine Tools and Manufacture 46: 381–386

    Article  Google Scholar 

  • Haykin S. (2003) Neural Networks: A comprehensive foundation. Pearson Education, Delhi

    Google Scholar 

  • Heyns P. S. (2007) Tool condition monitoring using vibration measurements—a review. Insight 49(8): 447–450

    Article  Google Scholar 

  • Jemielniak K., Kwiatkowski L., Wrzosek P. (1998) Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network. Journal of Intelligent Manufacturing 9: 447–455

    Article  Google Scholar 

  • JolliHe I. J. (1986) Principal Component Analysis. Springer, New York

    Google Scholar 

  • Keinert F. (2004) Wavelets and multiwavelets. Chapman and Hall/CRC, USA

    Google Scholar 

  • Lee J. M., Choi D. K., Kim J., Chu C. N. (1995) Real-time tool breakage monitoring for NC milling process. Annals of the CIRP 44(1): 59–62

    Article  Google Scholar 

  • Mannan M. A., Broms S. (1989) Monitoring and adaptive control of cutting process by means of motor power and current measurements. Annals of the CIRP 38(1): 347–350

    Article  Google Scholar 

  • Mathworks Inc. (2007). User manual of wavelet toolbox. MATLAB 7.0.

  • Mittermayr C. R., Nikolov S. G., Hutter H., Grasserbauer M. (1996) Wavelet denoising of Gaussian peaks: a comparative study. Chemometrics and Intelligent Laboratory Systems 34: 187–202

    Article  Google Scholar 

  • Obikawa T., Kaseda C., Matsumura T., Gong W. G., Shirakashi T. (1996) Tool wear monitoring for optimization cutting conditions. Journal of Material Processing Technology 62: 374–379

    Article  Google Scholar 

  • Pal S., Pal S. K., Samantaray A. K. (2007) Radial basis function neural network model based prediction of weld-plate distortion due to pulsed metal inert gas welding. Science and Technology of Welding and Joining 12(8): 725–731

    Article  Google Scholar 

  • Papadopoulos G., Edwards P. J. (2001) Confidence estimation methods for neural networks: a practical comparison. IEEE Transactions on Neural Networks 12(6): 1278–1287

    Article  Google Scholar 

  • Purushothaman, S. (2009). Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns. Journal of Intelligent Manufacturing. doi:10.1007/s10845-009-0249-y.

  • Rehorn A. G., Jiang J., Orban P. E. (2005) State-of-the-art methods and results in tool condition monitoring: a review. International Journal of Advanced Manufacturing Technology 26: 693–710

    Article  Google Scholar 

  • Scheffer C., Heyns P. S. (2004) An industrial tool wear monitoring system for interrupted turning. Mechanical Systems and Signal Processing 18: 1219–1242

    Article  Google Scholar 

  • Sick B. (2002) On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mechanical Systems and Signal Processing 16: 487–546

    Article  Google Scholar 

  • Velayudham A., Krishnamurthy R., Soundarapandian T. (2005) Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform. Material Science and Engineering A 412: 141–145

    Article  Google Scholar 

  • Wang, X., & Jawahir, I. S. (2001). Optimization of multi-pass turning operations using genetic algorithms for the selection of cutting conditions and cutting tools with tool-wear effect. In IFSA world congress and 20th NAFIPS international conference (IEEE) (vol. 5, pp. 3093–3100). Canada: Vancouver, BC.

  • Wang X., Wang W., Huang Y., Nguyen N., Krishnakumar K. (2008) Design of neural network-based estimator for tool wear modelling in hard turning. Journal of Intelligent Manufacturing 19: 383–396

    Article  Google Scholar 

  • Wu Y., Du R. (1996) Feature extraction and assessment using wavelet packets for monitoring of machining processes. Mechanical System and Signal Processing 10: 29–53

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to P. Stephan Heyns.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pal, S., Heyns, P.S., Freyer, B.H. et al. Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties. J Intell Manuf 22, 491–504 (2011). https://doi.org/10.1007/s10845-009-0310-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-009-0310-x

Keywords

Navigation